Computational-level Analysis of Constraint Compliance for General Intelligence

Human behavior is conditioned by codes and norms that constrain action. Rules, ``manners,'' laws, and moral imperatives are examples of classes of constraints that govern human behavior. These systems of constraints are ``messy:'' individual constraints are often poorly defined, what constraints are relevant in a particular situation may be unknown or ambiguous, constraints interact and conflict with one another, and determining how to act within the bounds of the relevant constraints may be a significant challenge, especially when rapid decisions are needed. Despite such messiness, humans incorporate constraints in their decisions robustly and rapidly. General, artificially-intelligent agents must also be able to navigate the messiness of systems of real-world constraints in order to behave predictability and reliably. In this paper, we characterize sources of complexity in constraint processing for general agents and describe a computational-level analysis for such \textit{constraint compliance}. We identify key algorithmic requirements based on the computational-level analysis and outline an initial, exploratory implementation of a general approach to constraint compliance.Comment: 10 pages, 2 figures. Accepted for presentation at AGI 2023 (revised in response to reviewer suggestions

Improving Language Model Prompting in Support of Semi-autonomous Task Learning

Language models (LLMs) offer potential as a source of knowledge for agents that need to acquire new task competencies within a performance environment. We describe efforts toward a novel agent capability that can construct cues (or "prompts") that result in useful LLM responses for an agent learning a new task. Importantly, responses must not only be "reasonable" (a measure used commonly in research on knowledge extraction from LLMs) but also specific to the agent's task context and in a form that the agent can interpret given its native language capacities. We summarize a series of empirical investigations of prompting strategies and evaluate responses against the goals of targeted and actionable responses for task learning. Our results demonstrate that actionable task knowledge can be obtained from LLMs in support of online agent task learning.Comment: Submitted to ACS 202

Learning in Tele-autonomous Systems using Soar

Robo-Soar is a high-level robot arm control system implemented in Soar. Robo-Soar learns to perform simple block manipulation tasks using advice from a human. Following learning, the system is able to perform similar tasks without external guidance. Robo-Soar corrects its knowledge by accepting advice about relevance of features in its domain, using a unique integration of analytic and empirical learning techniques

Robo-Soar: An Integration of External Interaction, Planning, and Learning using Soar

This chapter reports progress in extending the Soar architecture to tasks that involve interaction with external environments. The tasks are performed using a Puma arm and a camera in a system called Robo-Soar. The tasks require the integration of a variety of capabilities including problem solving with incomplete knowledge, reactivity, planning, guidance from external advice, and learning to improve the efficiency and correctness of problem solving. All of these capabilities are achieved without the addition of special purpose modules or subsystems to Soar

On Unified Theories of Cognition: a response to the reviews

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30999/1/0000674.pd

Knowledge-directed Adaptation in Multi-level Agents

Most work on adaptive agents have a simple, single layerarchitecture. However, most agent architectures support three levels ofknowledge and control: a reflex level for reactive responses, a deliberatelevel for goal-driven behavior, and a reflective layer for deliberateplanning and problem decomposition. In this paper we explore agentsimplemented in Soar that behave and learn at the deliberate and reflectivelevels. These levels enhance not only behavior, but also adaptation. Theagents use a combination of analytic and empirical learning, drawing from avariety of sources of knowledge to adapt to their environment. We hypothesize that complete, adaptive agents must be able to learn across all three levels.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46502/1/10844_2004_Article_146932.pd

A preliminary analysis of the Soar architecture as a basis for general intelligence

In this article we take a step towards providing an analysis of the Soar architecture as a basis for general intelligence. Included are discussions of the basic assumptions underlying the development of Soar, a description of Soar cast in terms of the theoretical idea of multiple levels of description, an example of Soar performing multi-column subtraction, and three analyses of Soar: its natural tasks, the sources of its power, and its scope and limitsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29595/1/0000684.pd

Ozone Depletion from Nearby Supernovae

Estimates made in the 1970's indicated that a supernova occurring within tens of parsecs of Earth could have significant effects on the ozone layer. Since that time, improved tools for detailed modeling of atmospheric chemistry have been developed to calculate ozone depletion, and advances have been made in theoretical modeling of supernovae and of the resultant gamma-ray spectra. In addition, one now has better knowledge of the occurrence rate of supernovae in the galaxy, and of the spatial distribution of progenitors to core-collapse supernovae. We report here the results of two-dimensional atmospheric model calculations that take as input the spectral energy distribution of a supernova, adopting various distances from Earth and various latitude impact angles. In separate simulations we calculate the ozone depletion due to both gamma-rays and cosmic rays. We find that for the combined ozone depletion roughly to double the ``biologically active'' UV flux received at the surface of the Earth, the supernova must occur at <8 pc. Based on the latest data, the time-averaged galactic rate of core-collapse supernovae occurring within 8 pc is ~1.5/Gyr. In comparing our calculated ozone depletions with those of previous studies, we find them to be significantly less severe than found by Ruderman (1974), and consistent with Whitten et al. (1976). In summary, given the amplitude of the effect, the rate of nearby supernovae, and the ~Gyr time scale for multicellular organisms on Earth, this particular pathway for mass extinctions may be less important than previously thought.Comment: 24 pages, 4 Postscript figures, to appear in The Astrophysical Journal, 2003 March 10, vol. 58